Predicting pregnancy-related pelvic girdle pain using machine learning.

Journal: Musculoskeletal science & practice
PMID: 40250138

Abstract

BACKGROUND: Pregnancy-related pelvic girdle pain (PPGP) is a common complication during gestation which negatively influences pregnant women's quality of life. There are numerous risk factors associated with PPGP, however, there is limited information about being able to predict the diagnosis of PPGP.

Authors

  • Atefe Ashrafi
    School of Health Sciences, Western Sydney University, Sydney, New South Wales, Australia. Electronic address: a.ashrafi@westernsydney.edu.au.
  • Daniel Thomson
    School of Health Sciences, Western Sydney University, Sydney, New South Wales, Australia.
  • Hadi Akbarzadeh Khorshidi
    School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia; School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.
  • Amir Marashi
    School of Medical Science, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
  • Darren Beales
    School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, WA, Australia; enAble Institute, Faculty of Health Sciences, Curtin University, Perth, WA, Australia.
  • Dragana Ceprnja
    Department of Physiotherapy, Westmead Hospital, Sydney, New South Wales, Australia; School of Science and Health, Western Sydney University, Sydney, New South Wales, Australia.
  • Amitabh Gupta
    School of Health Sciences, Western Sydney University, Sydney, New South Wales, Australia; Department of Physiotherapy, Westmead Hospital, Sydney, New South Wales, Australia.

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